# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Note that we don't combine the main with ray_trainer as ray_trainer is used by other main.
"""
from verl.trainer.ppo.ray_trainer import RayPPOTrainer

import ray
import hydra


@hydra.main(config_path='config', config_name='ppo_trainer', version_base=None)
def main(config):
    run_ppo(config)


def run_ppo(config, compute_score=None):
    if not ray.is_initialized():
        # this is for local ray cluster
        ray.init(
            # dashboard_port=3001,
            runtime_env={'env_vars': {'TOKENIZERS_PARALLELISM': 'true', 'NCCL_DEBUG': 'WARN', 'RAY_DEBUG': '0'}}
        )

    ray.get(main_task.remote(config, compute_score))


@ray.remote(num_cpus=1)  # please make sure main_task is not scheduled on head
def main_task(config, compute_score=None):
    from verl.utils.fs import copy_local_path_from_hdfs
    # print initial config
    from pprint import pprint
    from omegaconf import OmegaConf
    pprint(OmegaConf.to_container(config, resolve=True))  # resolve=True will eval symbol values
    OmegaConf.resolve(config)

    # download the checkpoint from hdfs
    local_path = copy_local_path_from_hdfs(config.actor_rollout_ref.model.path)

    # instantiate tokenizer
    from verl.utils import hf_tokenizer
    tokenizer = hf_tokenizer(local_path)

    # define worker classes
    if config.actor_rollout_ref.actor.strategy == 'fsdp':
        assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
        from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker
        from verl.single_controller.ray import RayWorkerGroup
        ray_worker_group_cls = RayWorkerGroup

    elif config.actor_rollout_ref.actor.strategy == 'megatron':
        assert config.actor_rollout_ref.actor.strategy == config.critic.strategy
        from verl.workers.megatron_workers import ActorRolloutRefWorker, CriticWorker
        from verl.single_controller.ray.megatron import NVMegatronRayWorkerGroup
        ray_worker_group_cls = NVMegatronRayWorkerGroup

    else:
        raise NotImplementedError

    from verl.trainer.ppo.ray_trainer import ResourcePoolManager, Role

    role_worker_mapping = {
        Role.ActorRollout: ray.remote(ActorRolloutRefWorker),
        Role.Critic: ray.remote(CriticWorker),
        Role.RefPolicy: ray.remote(ActorRolloutRefWorker)
    }

    global_pool_id = 'global_pool'
    resource_pool_spec = {
        global_pool_id: [config.trainer.n_gpus_per_node] * config.trainer.nnodes,
    }
    mapping = {
        Role.ActorRollout: global_pool_id,
        Role.Critic: global_pool_id,
        Role.RefPolicy: global_pool_id,
    }

    # we should adopt a multi-source reward function here
    # - for rule-based rm, we directly call a reward score
    # - for model-based rm, we call a model
    # - for code related prompt, we send to a sandbox if there are test cases
    # - finally, we combine all the rewards together
    # - The reward type depends on the tag of the data
    if config.reward_model.enable:
        if config.reward_model.strategy == 'fsdp':
            from verl.workers.fsdp_workers import RewardModelWorker
        elif config.reward_model.strategy == 'megatron':
            from verl.workers.megatron_workers import RewardModelWorker
        else:
            raise NotImplementedError
        role_worker_mapping[Role.RewardModel] = ray.remote(RewardModelWorker)
        mapping[Role.RewardModel] = global_pool_id

    reward_manager_name = config.reward_model.get("reward_manager", "naive")
    if reward_manager_name == 'naive':
        from verl.workers.reward_manager import NaiveRewardManager
        reward_manager_cls = NaiveRewardManager
        reward_fn = reward_manager_cls(tokenizer=tokenizer, num_examine=0, compute_score=compute_score, format_mode=config.reward_model.get('format_mode', 'R1'))
    elif reward_manager_name == 'prime':
        from verl.workers.reward_manager import PrimeRewardManager
        reward_manager_cls = PrimeRewardManager
        reward_fn = reward_manager_cls(tokenizer=tokenizer, num_examine=0, compute_score=compute_score)
    elif 'mix' in reward_manager_name:
        from verl.workers.reward_manager import MixRewardManager
        reward_manager_cls = MixRewardManager
        reward_fn = reward_manager_cls(
            tokenizer=tokenizer, num_examine=0,
            compute_exact_score_func=None,
            compute_fuzzy_score_name=config.reward_model.get('compute_score_name', None),
            shaping_function_name=config.reward_model.get('reward_manager_shaping_function_name', 'identity'),
            discrete_function_name=config.reward_model.get('reward_manager_discrete_function_name', 'identity'),
            n_rollouts=config.actor_rollout_ref.rollout.n,
            format_coefficient=config.reward_model.get('format_coefficient', 0.1),
            mix_type=config.reward_model.get('mix_type', 'hard'),
            pr_weight=config.reward_model.get('pr_weight', 0.5),
            vr_weight=config.reward_model.get('vr_weight', 1.0),
            format_mode=config.reward_model.get('format_mode', 'R1'),

        )
    elif 'prob' in reward_manager_name: # cross entropy
        from verl.workers.reward_manager import ProbRewardManager
        reward_manager_cls = ProbRewardManager
        reward_fn = reward_manager_cls(tokenizer=tokenizer, num_examine=0, 
                                       compute_score_name=config.reward_model.get('compute_score_name', None), 
                                       shaping_function_name=config.reward_model.get('reward_manager_shaping_function_name', 'identity'),
                                       discrete_function_name=config.reward_model.get('reward_manager_discrete_function_name', 'identity'),
                                       format_coefficient=config.reward_model.get('format_coefficient', 0.1),
                                       reward_type=config.reward_model.get('reward_type', 'pr'),
                                       gt_tokens_one_more=config.reward_model.get('gt_tokens_one_more', False), 
                                       gt_tokens_one_more_adjusted=config.reward_model.get('gt_tokens_one_more_adjusted', False),
                                       format_mode=config.reward_model.get('format_mode', 'R1'),
                                       )

    else:
        print(f"{reward_manager_name=}")
        raise NotImplementedError

    # Note that we always use function-based RM for validation
    val_reward_manager_name = config.reward_model.get("val_reward_manager", reward_manager_name)
    if val_reward_manager_name == 'naive':
        from verl.workers.reward_manager import NaiveRewardManager
        val_reward_manager_cls = NaiveRewardManager
        val_reward_fn = val_reward_manager_cls(tokenizer=tokenizer, num_examine=1, compute_score=compute_score, 
                                               save_results_dir=config.trainer.get('val_save_results_dir', None),
                                               phase='validation')
    elif val_reward_manager_name == 'prime':
        from verl.workers.reward_manager import PrimeRewardManager
        val_reward_manager_cls = PrimeRewardManager
        val_reward_fn = val_reward_manager_cls(tokenizer=tokenizer, num_examine=1, compute_score=compute_score, 
                                               save_results_dir=config.trainer.get('val_save_results_dir', None))
    else:
        print(f"{reward_manager_name=}")
        raise NotImplementedError



    resource_pool_manager = ResourcePoolManager(resource_pool_spec=resource_pool_spec, mapping=mapping)

    trainer = RayPPOTrainer(config=config,
                            tokenizer=tokenizer,
                            role_worker_mapping=role_worker_mapping,
                            resource_pool_manager=resource_pool_manager,
                            ray_worker_group_cls=ray_worker_group_cls,
                            reward_fn=reward_fn,
                            val_reward_fn=val_reward_fn)
    print('Initialized RayPPOTrainer, starting init workers...')
    trainer.init_workers()
    print('Initialized workers, starting training...')
    trainer.fit()


if __name__ == '__main__':
    main()
